• The Transactions of the Korea Information Processing Society
• /
• v.6 no.12
• /
• pp.3703-3709
• /
• 1999

We propose a new approach to reconstruct the surface shape of an object from a shaded image. We use genetic algorithm instead of gradient descent algorithm which is apt to take to local minima and also proposes genetic representation and suitable genetic operators for manipulating 2-D image. And for more effective execution, we suggest hierarchical process to reconstruct minutely the surface of an object after coarse and global reconstruction. A modified Lambertian illumination model including the distance factor was herein adopted to get more reasonable result and an experiment was performed with synthesized and real images to demonstrate the devised method, of which results show the usefulness of our method.

• 제어로봇시스템학회:학술대회논문집
• /
• 1991.10b
• /
• pp.1939-1943
• /
• 1991

Path planning is an important task for optimal motion of a robot in structured or unstructured environment. The goal of this paper is to plan the shortest collision-free path in 3D, when a robot is navigated to pick up some tools or to repair some parts from various locations. To accomplish the goal of this paper, the Path Coordinator is proposed to have the capabilities of an obstacle avoidance strategy[3] and a traveling salesman problem strategy(TSP)[23]. The obstacle avoidance strategy is to plan the shortest collision-free path between each pair of n locations in 2D or in 3D. The TSP strategy is to compute a minimal system cost of a tour that is defined as a closed path navigating each location exactly once. The TSP strategy can be implemented by the Neural Network. The obstacle avoidance strategy in 2D can be implemented by the VGraph Algorithm. However, the VGraph Algorithm is not useful in 3D, because it can't compute the global optimality in 3D. Thus, the Path Coordinator is proposed to solve this problem, having the capabilities of selecting the optimal edges by the modified Genetic Algorithm[21] and computing the optimal nodes along the optimal edges by the Recursive Compensation Algorithm[5].

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.55 no.6
• /
• pp.242-248
• /
• 2006

This paper proposes a coordinated control method for under-load tap changers (ULTCs) with shunt capacitors to reduce the operation numbers of both devices. The proposed method consists of two stages. In the first stage, the dispatch schedule is determined using a genetic algorithm with forecasted loads to reduce the power loss and to improve the voltage profile during a day. In the second stage, each capacitor operates according to this dispatch schedule and the ULTCs are controlled in real time with the modified reference voltages considering the dispatch schedule of the capacitors. The performance of the method is evaluated for the modified IEEE 14-bus system. Simulation results show that the proposed method performs better than a conventional control method.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.23 no.2
• /
• pp.187-195
• /
• 2012

In this paper, we propose the optimization of subarray configurations for linear array to minimize the side lobe level (SLL) in sum beam pattern based on the genetic algorithm. The operations of genetic algorithm are modified to be applied to subarray configurations. Using the proposed method, we construct subarray structure with 16 irregular subarray elements from 40 linear array elements to minimize the SLL in sum beam pattern in case of applying the adaptive beamforming(ABF) to suppress the jamming power, whose the SLL is 10 dB lower than that of regular subarray configuration.

• 제어로봇시스템학회:학술대회논문집
• /
• 2002.10a
• /
• pp.70.1-70
• /
• 2002

In the fuzzy applications and theories, it is very important to consider how to design the optimal fuzzy model from short training data, in order to construct the reasonable fuzzy model for identifying the practical process. There are several concerns to be confirmed for efficient fuzzy model design. One of concern is the optimization problem of the fuzzy model. In various applications, the genetic algorithm is widely applied to obtain optimal fuzzy model and other cases that adopt evolutionary mechanism of the nature. If we use natural selection and multiplication operation of the genetic algorithm, early convergence to local minimum can be occurred. In other word, we can find only optimum...

• Proceedings of the KIEE Conference
• /
• 2002.11c
• /
• pp.214-217
• /
• 2002

The chromosomes of Genetic Algorithm(GA) are classified to be good or not to be by Support vector machines(SVM), and then the only good chromosomes are adopted to the evolution process. By this way, computational load becomes low, so the evolution speed of Genetic Algorithm modified by SVM can be much accelerated than the conventional GA.

• Proceedings of the KIEE Conference
• /
• 2002.11c
• /
• pp.513-516
• /
• 2002

In electrical impedance tomography(EIT), various image reconstruction algorithms have been used in order to compute the internal resistivity distribution of the unknown object with its electric potential data at the boundary. Mathematically the EIT image reconstruction algorithm is a nonlinear ill-posed inverse problem. This paper presents two intelligent optimization algorithm techniques such as genetic algorithm and simulated annealing for the solution of the static EIT inverse problem. We summarize the simulation results for the three algorithm forms: modified Newton-Raphson, genetic algorithm, and simulated annealing.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2004.11a
• /
• pp.459-462
• /
• 2004

This paper proposes a genetically inspired adaptive clustering algorithm. The algorithm automatically discovers the actual number of clusters and efficiently performs clustering without unduly compromising cluster purity. Chromosome encoding that ensures the correct number of clusters and cluster purity is discussed. The required fitness function is desisted on the basis of modified similarity criteria and genetic operators. These are incorporated into the proposed adaptive clustering algorithm. Experimental results show the efficiency of the clustering algorithm on synthetic data sets and real world data sets.

• Journal of Korea Multimedia Society
• /
• v.11 no.12
• /
• pp.1625-1634
• /
• 2008

In this study, a modified Fuzzy C-Means algorithm with Gaussian weights (MFCM_GW) is presented for the problem of nonlinear blind channel equalization. The proposed algorithm searches for the optimal channel output states of a nonlinear channel based on received symbols. In contrast to conventional Euclidean distance in Fuzzy C-Means (FCM), the use of the Bayesian likelihood fitness function and the Gaussian weighted partition matrix is exploited in this method. In the search procedure, all possible sets of desired channel states are constructed by considering the combinations of estimated channel output states. The set of desired states characterized by the maxima] value of the Bayesian fitness is selected and updated by using the Gaussian weights. After this procedure, the Bayesian equalizer with the final desired states is implemented to reconstruct transmitted symbols. The performance of the proposed method is compared with those of a simplex genetic algorithm (GA), a hybrid genetic algorithm (GA merged with simulated annealing (SA):GASA), and a previously developed version of MFCM. In particular, a relative]y high accuracy and a fast search speed have been observed.

• Journal of the Korean Institute of Telematics and Electronics C
• /
• v.36C no.8
• /
• pp.54-61
• /
• 1999

In this paper, we apply one of modified Genetic Algorithms, a Multi-population Genetic Algorithm(MGA) that improves the genetic diversity to determine the fuzzy rule base and the shape of membership functions. The generation of the fuzzy rule base for fuzzy control, generally, depends on expert's experience. We suggest a new evaluation function to optimize fuzzy rule base. Simulation shows that the proposed method has good result.